Supported by eye-movement data collected during a controlled experiment on small-multiple map displays, a new concept coined inference affordance aimed at overcoming drawbacks of traditional empirical 'success' measures when evaluating static visual analytics displays and interactive visual analytics tools is proposed. Then, a novel visual analytics research methodology is outlined to quantify inference affordance, taking advantage of the well-known sequence alignment analyses techniques borrowed from bioinformatics. The presented visual analytics approach focuses on information reduction of large amounts of fine-grained eye-movement sequence data, including sequence categorisation and summarisation.

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